Supervised Machine Learning Models for the Prediction of Renal Failure in Senegal

The objective of this paper goal is to propose models to predict the diagnosis of acute, chronic or terminal chronic renal failure using supervised machine learning methods. We used medical data from the Nephrology Service of the Aristide Le Dantec Hospital in Dakar to build prediction models with supervised learning methods: Multilayer Perceptron, Support Vector Machines, Random Forest and Extreme Gradient Boosting. The model built with Extreme Gradient Boosting proved to be the best according to the performance measures we used, namely precision, recall, F1-score and accuracy. Follow, in order, the models built with Random Forest, Logistic Regression, Support Vector Machines and Multilayer Perceptron.